Automated fungal spore count
Automated fungal Spore Counting
Counting fungal spores is an essential task in various scientific disciplines, including mycology, ecology, crop science, food science and medicine. Traditionally, fungal spores have been counted manually under a microscope, but with the advancement of the µCount3D technology, a new, easy and automated counting technology is available.
The µCount3D from BioSense Solutions is developed specifically for microbiology and will accommodate counts of even the smallest of fungal spores.
How we image
The patented FluidScope technology is a tilted camera technology. When images are taken, we get to image a volume instead of a plane. Every image has a height of 150µm and since images overlap we get to create both a vertical and horizontal z-stack. All objects present in this volume, is captured in focus.
For fungal spore counting we image the bottom of a µCassetteF. An image consisting of 400 overlapping images is created and specific deep learning algorithms will count the number of spores present. The µCassetteF sample chambers have an inner height of 200µm allowing fungal spores to settle in just 4 minutes.
The µCassetteF is developed with triplicate sample chambers and the Count3D software will provide fungal spores/ml and supply images for documentation.
µCount3D Specifications
- Species: Bacteria, Fungi, Yeast and Algae
- Size: H: 20cm W: 10cm D: 20cm
- Weight: 3kg
- Power consumption: Standby 7W, Running 16,8W
- Microbe size: from 0,5µm
- Countable Range: 1 x 104 – 1 x 107
- 3 chamber time to result: ~8 minutes
- Output: Organisms/ml, images for documentation, PDF Report
- Sample containers: BioSense Solutions triplicate µCassetteF & µCassetteB
- Create your own specialized algorithms
Algorithms
In the software you can choose between a range of species. These are all specific algorithms, trained on +2000 sub images from each species. Algorithms are trained to find spores and not mycelium or other particles. Algorithms cover most morphological shapes of fungal spores and if your species is not on the list another algorithm will probably do. Example, if your spore is characterized by having a pill shape, then the Metarhizium algorithm will most likely do a very good job. Many users have a specific background, being product from an antifungal, medium residue, or it can be an environmental sample. In such cases, we always recommend to train a specific algorithm for your specific sample matrix.
Algorithm Development
We work closely with the fungal research community and will be adding new deep learning algorithms for new species and backgrounds continuously. However, we will never cover all needs and you can create your own algorithm using our Annotation Tool. The Annotation Tool It is a stand alone programme, where you annotate the spores in single images. Some can be easy while others will be more complex. Spore populations like Fusarium can exhibit a high degree of heterogeneity and these will take time. Being thorough and consistent is key when annotating. Process is simple – either you annotate or we annotate based on your supplied images. Algorithms developed can further be proprietary or for the common good. On the right is a screen shot from the Annotation Tool training a new algorithm for Cladosporium allicinum in a complex matrix.
Count3D Software
The Count3D software has been developed with the purpose that all people should be able to operate it. In just 5 easy steps you set up your sample and insert the µCassetteF. No pretreatment is needed. You may use 1, 2 or all sample chambers and dilutions can be typed in as well. When count is complete, you will be presented with results and images. This can further be exported as a pdf report containing details and images from your count. Report can then be stored, making the µCount3D an ideal instrument for QC departments or other that require all data to be stored.
Adjusting concentrations for lab experiments
Want to know your concentration of Aspergillus brasiliensis spores or other fungal species? The µCount3D is developed to help your lab accurately count your microbial populations in minutes. It is easy, precise, non-biased, and fast.
From a sample with a known concentration of 1.1E+8 a serial dilution was prepared in Eppendorf tubes. Serial dilution was intended span the range from approx. 1.0E+7 to 2.0E+4. From this series, 30 µl sample was pipetted in triplicate using the µCassetteF from BioSense Solutions. Spores/ml was established using the µCount3D.
QC - Yeast. Counting Chamber Vs. µCount3D
A 2 fold dilution series of yeast cells was prepared in Eppendorf tubes (0.9% NaCl buffer). First, dilutions was manually counted using a C-Chip, Neubacher Improved, DHC-N01 counting chamber. C-chip was counted using a Zeiss Axioskop 2 plus. One well per dilution. Once counted manually, same samples were pipetted into a triplicate chamber µCassetteF and counted using the µCount3D. Yeast counting algorithm was chosen.
Results: Counting results from the µCount3D was higher than the manual count in initial sample (Sample 1). Looking at the actual numbers in the 2 fold dilution suggest that “manual count 1” was too low and should have been around ~4.40E+06 if it was to correlate with manual counts 2, 3, 4. Counts from the µCount3D had a good correlation in between samples.
Operator comment: it was difficult to count high concentrations of cells in the manual counting chamber, and that this might account for the discrepancy between counts.
QC - Coniothyrium. Counting Chamber Vs. µCount3D
A 2 fold dilution series of Coniothyrium spores was prepared in Eppendorf tubes (0.9% NaCl buffer). First, dilutions was manually counted using a C-Chip, Neubacher Improved, DHC-N01 counting chamber. C-chip was counted using a Zeiss Axioskop 2 plus. One well per dilution. Once counted manually, same samples were pipetted into a triplicate chamber µCassetteF and counted using the µCount3D. Coniothyrium spores counting algorithm was chosen.
Results: Counting results from the manual count was higher than the counts using the µCount3D. There is a good correlation between the 2 dilutions in both counts.
Operator comment: Coniothyrium spores tend to aggregate and it was difficult to count spores in aggregates. The µCount3D Coniothyrium algorithm is trained to count what a human eye can see and if there are aggregates, spores in these are not counted. This probably explains difference between counts.
QC Data
FAQ
- What is the volume used in µCassetteF? 30µl is pipetted into each chamber of the µCassetteF
- Can you ship all over the world? The µCount3D complies with international standards and is certified for use in Asia, Europe, the US and the Americas.
- Can I use a Macbook? No, the µCount3D is developed for PC only and minimum requirements are as follows:
Minimum Requirements
Processor: Intel i7 1800 Mhz
Hard disk: 512 GB, solid-state (an external SSD can be used)
Network card: 1 Gb/s
GPU: NVIDIA RTX series
Memory: 16 GB
OS: Windows 11
Tested on:
Dell Precision 3580
Processor: 13th Gen Intel(R) Core(TM) i7-1370P, 1900 Mhz, 14 Core(s), 20 Logical Processor(s)
Hard disk: 1 TB, solid-state
Network card: 1 Gb/s
Memory: 32GB
OS: Windows 11 pro
- Do you have re-usable µCassettes? No, currently we do not sell re-usable µCassettes.
- Can I count co-culture sample? The µCount3D is developed to count single species populations of fungal spores. However, user can run an algo for one species (morphology) and then another species (morphology) and get a count for two species in one sample. It is obvious that morphology needs to be different for the 2 species.
- Do I need to sterile filtrate my medium or buffer if I have MilliQ water? As we use deep learning algorithms for fungal spore counting there is no need to have a super clean sample matrix. Normal medium should do fine.
- Can I use any medium? Yes, as long a medium is transparent.
- Is the µCount3D compatible with the oCelloScope platform? Yes, you can export counting jobs and images to UniExplorer.
- Can I use flourescent dyes in the µCount3D? The µCount3D is based on Bright Field Microscopy and will not excite or pick up flourescent signals.
Benefits of Automation in Spore Counting
Automating you spore counts will increase efficiency, consistency, and precision. With automation, your counting methods will rapidly analyze large numbers of spores within a sample. This significantly reduces the time and effort required for counting. Automation will also offer consistency in counting, eliminating the variability introduced by human observers. Results are reproducible and less prone to bias.
Automation technologies can be based on: Imaging, Laser light scattering and Electrical impedance.
- Imaging counters. A camera will image the fungal spore population and algorithms will count spores presents in a given volume and provide images of findings.
- Laser light scattering. Fungal spores are passed by a light beam one by one, and the scattering of light will reveal information about morphological features and count the sample.
- Electrical impedance. Fungal spores are passed by two electrodes one by one and the difference in electrical current (impedance of objects) will reveal information about morphological features and count the sample.
Choosing the Right Technology
There can be plusses and minuses for all technologies, but there is no doubt that Laser light scattering will present the more expensive and difficult to use technology of the three. This is in the form of a flow cytometer. Electrical impedance and Imaging systems are less expensive and for daily counting of cells these technologies should cover most needs.
Imaging systems present a further advantage of providing the user with images of what has actually been counted. Even if technologies might count the same, seeing is believing, and images can often be used for additional analysis in other image analysis software programs such as FIJI or other.
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